JSEQ_scRNAseq - REPORT
2025-11-27
The report was generated using the JSEQ_scRNAseq single-cell analysis pipeline. For more information, please visit: https://github.com/jkubis96/JSEQ_scRNAseq
1 Analysis Configuration Parameters
| Parameter | Value |
|---|---|
| mt_per | 25 |
| scale_factor | 1e+06 |
| n_features | 1500 |
| c_res | 0.5 |
| heterogeneity | var |
| mt_cssg | FALSE |
| m_val | 0.05 |
| top_m | 50 |
| max_genes | 1000 |
| max_combine | 1000 |
| loss_val | 0.05 |
| s_factor | 0.7 |
| p_bin | 0.1 |
| drop | TRUE |
| min_c | 10 |
The parameters above were used during the current analysis.
If you need to apply different analysis conditions, modify the parameters in the configuration file.
Parameters description:
- mt_per
Maximum percentage of mitochondrial genes per cell.
Default: 25% - down
Lower threshold for the number of genes per cell.
Default: NA (automatically computed if NA) - up
Upper threshold for the number of genes per cell.
Default: NA (automatically computed if NA) - scale_factor
Scale factor used for data normalization. - n_features
Number of variable features to detect for clustering. - c_res
Clustering resolution — higher values produce more clusters. - heterogeneity
Method for estimating cluster heterogeneity.
Options:var(within-cluster variance),deg(deregulated gene profiles).
Default: deg - mt_cssg
Whether to include mitochondrial genes when creating subclasses and subtypes.
Options:TRUE,FALSE.
Default: FALSE - m_val
Maximum p-value threshold for marker detection in advanced subtype analyses.
Default: 0.05 - top_m
Maximum number of top markers used for naming clusters based on effect size metrics.
Default: 50 - max_genes
Maximum number of input genes considered in cluster heterogeneity discovery.
Default: 1000 - max_combine
Maximum number of initial combinations for each iteration during heterogeneity discovery.
Default: 1000 - loss_val
Assigned value for potentially unclassified cells within a cluster. - s_factor
Maximum split factor for gene occurrence in heterogeneity discovery (0.2–1).
Default: 0.8 - p_bin
Minimum cell proportion required for population presence based on binomial test p-value.
Default: 0.05 - min_c
Minimum cell proportion required for population presence as defined by the user (independent of binomial test).
Default: 10 - drop
Boolean indicating whether to drop non-significant subtypes based onp_binandmin_c.
Default: TRUE
The configuration file is available in the directory:
JSEQ_scRNAseq/requirements_file/config_file.conf
Additional configuration files for other analysis steps can also be found in:
JSEQ_scRNAseq/requirements_file
For more information, please visit:
https://github.com/jkubis96/JSEQ_scRNAseq
2 Cell content analysis
2.1 Read distribution across genomic features
2.2 Count (UMI) per barcode (cell)
2.3 Cell barcode knee plot
The above graph illustrates general trends and quality metrics at the initial stage of single-cell analysis; however, it does not represent the final number of detected cells.
2.4 Ratio of number of genes to counts
2.5 Number of genes and counts per cell
2.6 Percentage of ribosomal and mitochondrial genes [%]
3 Quality control of cell content
3.1 Genes per cell content & thresholds
3.2 Genes upper & lower thresholds per cell
3.3 Number of cells across different stages of analysis
4 Gene expression analysis across cells
4.1 Top highly variable genes in the dataset
5 Princilpe component selection
5.1 ElbowPlot - principal components cutoff
5.2 JackStrawPlot - PC significance
